System and Method for Analysis of a Fluid

ABSTRACT

A system for analysing a fluid is described, including an in-line sensor configured to analyse a fluid flowing past the in-line sensor to determine at least one in-line value of a fluid parameter of the fluid across an event period, and a sample sensor configured to analyse a sample of fluid extracted from the flow of fluid during the event period, to determine a sample value of the fluid parameter for the sample. At least one processor is provided, configured to determine a representative in-line value of the fluid parameter across the event period based at least in part on the at least one in-line value, and determine an overall representative value of the fluid parameter across the event period based on the representative in-line value, the sample value for the sample, and one or more of the in-line values corresponding to the time of extracting the sample, wherein determination of the overall representative value is based on an error correction value determined for the in-line sensor during the event period.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application of U.S. application Ser. No.16/761,678 filed May 5, 2020 which claims priority PCT Application No.NZ2018/050153 filed 31 Oct. 2018 which claims priority to provisionalNew Zealand Patent Application No. 737052 filed 7 Nov. 2017, each ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

The present disclosure relates to a system and method for analysis of afluid—more particularly for analysis of the fluid using an in-linesensing device in combination with a sample sensing device, and moreparticularly for analysis of milk.

The use of sensors to obtain information relating to milk collected fromdairy animals is well known. Such information is used in decision makingregarding such matters as processing of the milk, culling, breeding,medical treatment, animal specific feed rations as well as measurementof milk production efficiency.

Numerous portable off-line analysers are known in the art for analysinga sample of milk to determine parameters such as fat, protein, lactoseand total solids. Examples of such analysers using ultrasound analysisinclude the LactiCheck™ milk analyser by Page & Pedersen International,Ltd (www.pagepedersen.com); the Master milk analyser by Milkotester Ltd(www.milkotester.com); the LACTOSCAN™ milk analyser by Milkotronic Ltd(www.lactoscan.com). Other analytical techniques are also known, forexample mid-infrared spectroscopy, as exemplified by the MIRIS™ DairyMilk Analyzer by Miris Holding AB (www.mirissolutions.com).

Such off-line analysers are generally capable of relatively highprecision measurements in comparison with commercially available in-linesensors—but have practical limitations associated with the requirementthat the analysis be performed on a discrete sample. In particular,measurements from the sample may not be representative of the milkparameter across the entire milking. For example, the variability of fatcontent across the course of a milking is such that a spot sample isunlikely to be representative of the average fat value of milkcollected.

In-line sensors are also known for use in the measurement of parametersof the milk flowing through them, without the requirement for a sampleto be collected and delivered to them. As such, they are capable ofcollecting data across the entirety of the milking. However, in order toachieve an acceptable price-point, and to meet the constraints imposedby flowing milk, such in-line sensors are generally of lower precisionthan the off-line analysers performing ultrasound or mid-infraredspectroscopy analysis.

It is an object of the present invention to address the foregoingproblems or at least to provide the public with a useful choice.

All references, including any patents or patent applications cited inthis specification are hereby incorporated by reference. No admission ismade that any reference constitutes prior art. The discussion of thereferences states what their authors assert, and the applicants reservethe right to challenge the accuracy and pertinency of the citeddocuments. It will be clearly understood that, although a number ofprior art publications are referred to herein, this reference does notconstitute an admission that any of these documents form part of thecommon general knowledge in the art, in New Zealand or in any othercountry.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise”, “comprising”, and thelike, are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense, that is to say, in the sense of“including, but not limited to”.

Further aspects and advantages of the present invention will becomeapparent from the ensuing description which is given by way of exampleonly.

BRIEF SUMMARY OF THE INVENTION

According to one aspect of the present disclosure, there is provided asystem for analysing a fluid. The system includes an in-line sensorconfigured to analyse a fluid flowing past the in-line sensor todetermine at least one in-line value of a fluid parameter of the fluidacross an event period. The system includes a sample sensor configuredto analyse a sample of fluid extracted from the flow of fluid during theevent period, to determine a sample value of the fluid parameter for thesample. The system includes at least one processor configured todetermine a representative in-line value of the fluid parameter acrossthe event period based at least in part on the at least one in-linevalue. The at least one processor is further configured to determine anoverall representative value of the fluid parameter across the eventperiod based on the representative in-line value, the sample value forthe sample, and one or more of the in-line values corresponding to thetime of extracting the sample, wherein determination of the overallrepresentative value is based on an error correction value determinedfor the in-line sensor during the event period.

According to one aspect of the present disclosure, there is provided amethod for analysing a fluid. The method includes the step of analysinga fluid flowing past an in-line sensor to determine at least one in-linevalue of a fluid parameter of the fluid across an event period. Themethod further includes the step of analysing, with a sample sensor, asample of fluid extracted from the flow of fluid during the eventperiod, to determine a sample value of the fluid parameter for thesample. The method further includes the step of determining arepresentative in-line value of the fluid parameter across the eventperiod, based at least in part on the at least one in-line value. Themethod further includes the step of determining an overallrepresentative value of the fluid parameter based on the representativein-line value, the sample value for the sample, and one or more of thein-line values corresponding to the time of extracting the sample,wherein determination of the overall representative value is based on anerror correction value determined for the in-line sensor during theevent period.

According to one aspect of the present disclosure, there is provided asystem for analysing a fluid. The system includes an in-line sensorconfigured to analyse a fluid flowing past the in-line sensor todetermine at least one in-line value of a fluid parameter of the fluidacross an event period. The system includes a sample sensor configuredto analyse a sample of fluid extracted from the flow of fluid during theevent period, to determine a sample value of the fluid parameter for thesample. The system includes at least one processor configured todetermine a representative in-line value of the fluid parameter acrossthe event period based at least in part on the at least one in-linevalue. The at least one processor is further configured to determine acorrected representative value of the fluid parameter based on therepresentative in-line value, the sample value for the sample, and oneor more of the in-line values corresponding to the time of extractingthe sample.

According to one aspect of the present disclosure, there is provided amethod for analysing a fluid. The method includes the step of analysinga fluid flowing past an in-line sensor to determine at least one in-linevalue of a fluid parameter of the fluid across an event period. Themethod further includes the step of analysing, with a sample sensor, asample of fluid extracted from the flow of fluid during the eventperiod, to determine a sample value of the fluid parameter for thesample. The method further includes the step of determining arepresentative in-line value of the fluid parameter across the eventperiod, based at least in part on the at least one in-line value. Themethod further includes the step of determining a correctedrepresentative value of the fluid parameter based on the representativein-line value, the sample value for the sample, and one or more of thein-line values corresponding to the time of extracting the sample.

Various configurations of sensors, in terms of how the sensor is exposedto the fluid to be analysed, are known in the art. Terms such as“in-line”, “on-line”, “at-line”, “near-line” and “off-line” are used inthe art to distinguish between these configurations—however there is adegree of inconsistency in their usage. Reference will be made herein to“in-line”, “on-line”, “off-line”, and “sample” sensors, which forclarity may be defined as follows.

Reference herein to an in-line sensor should be understood to mean asensor analysing fluid flowing past one or more sensing means, todetermine at least one parameter of the fluid at a particular point orperiod of time—i.e. without collection of a discrete sample from theflow.

Reference to an on-line sensor should be understood to mean a sensorwhich automatically extracts a sample of fluid from a fluid flow, andanalyses the sample of fluid to determine at least one parameter of thesample. As used herein, the term “on-line” may encompass embodiments inwhich the sample is returned to the fluid flow, or discarded.

The terms “at-line” and “off-line” may be used in the art to distinguishbetween the environment in which the sensor is configured to operate.Both at-line and off-line sensors are configured to analyse a discretesample of the fluid delivered to the sensor by an operator. At-linesensors (which may be referred to as “near-line” sensors) are generallyintended to be located within the vicinity of the fluid flow—forexample, within a milking facility—while off-line sensors are primarilyintended for use in a more environmentally controlled environment—forexample, in a laboratory. In practice, particularly for milkingoperations, analysis of a sample by an off-line sensor may necessitatetransport of the sample from the sample source to a remote facility. Asused herein, the term “off-line” should be understood to refer to asensor configuration in which a sample is collected from the fluid, anddelivered to the sensor by an operator rather than an automated system.

On-line and off-line sensors, as defined herein, may be distinguishedfrom in-line sensors by the act of analysing a sample extracted from thefluid flow rather than analysing the flow itself. As such, on-line andoff-line sensors may be referred to in the collective as “sample”sensors.

In exemplary embodiments, the fluid may be milk extracted from a milkinganimal. It is envisaged that the present disclosure may have particularapplication to the analysis of milk during the transfer of milk from thepoint of extraction to a storage vessel. Milking plants typicallyinclude individual milk transport conduits from the points of extraction(for example, using a milking cluster including teat cups), joining to acommon transport line for delivery to the storage vessel. The provisionof in-line sensors within the individual milk transport conduits isknown in the art—allowing for the analysis of milk extracted from anindividual animal as it flows through those individual milk transportconduits.

However, it is envisaged that exemplary embodiments of the presentdisclosure may have application to other fluid types—particularly wherethe fluid is transported via a conduit, and has a parameter which variesover an event period. Exemplary embodiments of the present disclosuremay have particular application to instances in which there is potentialfor bias in an in-line sensor measurement to change between eventperiods.

Reference to an event period should be understood to mean a period oftime associated with an event, during which it is desirable todistinguish the value of the fluid parameter of the flow of fluid fromthe value of the fluid parameter during another period of time—i.e. theflow of fluid includes a plurality of event periods associated withdiscrete events, and it is desirable to analyse the fluid for each eventperiod. It is also contemplated that the event period may be a period oftime over which the average value of the parameter is representative ofthe average value over a longer time period—i.e. the event period is asub-period within the entirety of the fluid flow.

For example, in the context of milking, the event period may be themilking of an individual animal. This allows for data to be collectedwhich relates to that particular animal.

Numerous such in-line sensors are known for use in relation to milkinganimals. Non-limiting examples may include: composition sensors thatmeasure properties of milk produced by an animal such as fat, protein,lactose, solids—not fat, and/or water content; yield sensors thatmeasure a volume and flow-rate of milk produced by an animal; and milkconductivity sensors to identify animals suffering from mastitis. By wayof example, the in-line sensor may be the YieldSense™ volume, fat, andprotein sensor by LIC Automation Limited (www.licautomation.co.nz), orthe AfiLab™ fat, protein and lactose concentration sensor by Afimilk Ltd(www.afimilk.com).

It is envisaged that exemplary embodiments of the present applicationmay have particular application to the determination of fat content inmilk. However, it should be appreciated that this is not intended to belimiting to all exemplary embodiments, and it is contemplated that otherparameters of milk may be determined—for example lactose and proteincontent—or parameters of fluids other than milk.

The fact that in-line milk sensors analyse the milk as it flows pastthem prevents the use of sample treatments that can improve measurement.For example, known ultrasound milk analysers control milk temperatureprecisely to achieve higher precision measurement. Known mid-infraredanalysers also control milk temperature and require a measurement cellmuch narrower than typical conduits for milk flow in which in-linesensors are positioned. Other treatments—including elimination of airbubbles, addition of reagents, and homogenisation—can be used in samplesensors but not in-line sensors, and may improve measurementperformance. Furthermore, sample sensors can be fabricated usingmaterials and geometries that do not meet hygiene requirements formilking systems and therefore cannot be used for in-line sensors. Theselimitations of in-line sensors contribute to their relatively lowprecision.

In-line sensors for milk take instantaneous readings of characteristicssuch as electric conductivity and optical properties and apply models todetermine a value for one or more milk parameters at the time at whichthe instantaneous readings were obtained. For completeness, it should beunderstood that reference to sensing optical properties of a fluid mayinclude sensing of properties with a sensor using electromagneticradiation which is not within the visible spectrum (i.e. the in-linesensor may be an electromagnetic radiation-based sensor). Typically,measurements by in-line sensors rely on milk characteristics that aredependent on multiple attributes of the milk being analysed. Some ofthese milk attributes are unknown to the sensor and cannot be correctedfor, resulting in measurement error. Some of the milk attributescontributing to measurement error in typical in-line milk sensors arebelieved to be relatively constant throughout the course of a milking,resulting in a relatively constant error throughout the course of amilking compared to the variation in error between event periods (i.e.between milkings).

In an exemplary embodiment, the in-line sensor is configured to obtain aplurality of in-line values of the fluid parameter across the eventperiod. It should be appreciated that reference to the in-line sensordetermining a plurality of in-line values of the fluid parameter isintended to encompass embodiments in which a continuous measurement isobtained (and discrete in-line values corresponding to the sample valuesare obtained from the continuous measurement), as well as embodiments inwhich discrete measurements are made repeatedly (whether periodically orintermittently) at a sufficient rate to allow the representative in-linevalue to be determined.

In exemplary embodiments, the sample sensor may be configured to extracta sample of the fluid from the flow of fluid during the event periodusing a sample extraction device—i.e. may be an on-line sensor.

As such, the on-line sensor may include a sample extraction deviceconfigured to extract the sample, and a sensing device configured toreceive and analyse the sample. It should be appreciated that inexemplary embodiments the components of the sample extraction device andsensing device may be realised in a single unit. It is also envisagedthat in an exemplary embodiment the sample sensor and the in-line sensormay be realised in a single unit.

The sample extraction device may include extraction means—for exampleone or more pumps, such as peristaltic pumps—to draw fluid from thefluid flow, and deliver it to the sensing device. The sample extractiondevice may include a sample collection chamber for conditioning thesample of fluid prior to delivery to the sensing device—for example byallowing settling of the fluid, and/or removal of a portion of thefluid. For example, in the analysis of a milk sample, air and milkbubbles rising to the top of the sample within the sample collectionchamber may be removed, or permitted to exit.

The ability to analyse a sample of the fluid, rather than in-flow aswith an in-line sensor, allows for use of sensing methodologies whichare not currently viable for in-line sensors under conditions such asthose experienced during (or required for) milking of an animal. Forexample, measurement techniques using ultrasound, acoustics,electromagnetic radiation (for example, near-infrared, or mid-infrared),and electronic impedance, are known for use in analysis of samples ofmilk, providing a higher precision determination of the targetedparameter. By way of example, the sensing device of the on-line sensormay implement the sensing methodology performed by the off-lineLactiCheck™ milk analyser by Page & Pedersen International, Ltd(www.pagepedersen.com) or the off-line MIRIS™ Dairy Milk Analyzer byMiris Holding AB (www.mirissolutions.com).

However, the analysis of a discrete sample has the potential to producea value for a fluid parameter which is a poor representation of thevalue across the event period—particularly for fluid parameters thathave significant variability across the event period. In the context ofmilking, the inventors have identified that fat content of milk is onesuch parameter. One possible way to address this limitation could be toobtain a proportional representative sample of the fluid across theevent period. However, a sample collection device capable of collectinga proportional representative milk sample, that will function reliablywithout human intervention and clean itself effectively during themilking system wash, is believed to be likely to add substantial costand complexity to the sensor. The inventors consider that such a samplermay be undesirable for these reasons. Further, this would requireanalysis of the sample at the end of milking—whereas it may be desirableto analyse the milk prior to this, in order to enable decision makingregarding management of the animal, based on the analysis, prior to theanimal exiting the milking facility.

In an exemplary embodiment, the distance between the in-line sensor andpoint of extraction of the sample along the fluid flow may be minimisedsuch that the time for the fluid to flow between these points isinsignificant for the purpose of determining the one or more in-linevalues corresponding to the sample value. However, it should beappreciated that reference to the one or more of the in-line valuescorresponding to the time of extracting the sample is intended toencompass embodiments in which the in-line sensor is positioned at apoint along the fluid flow distal from the point of extraction of thesample, and the time for the fluid to flow between these points is ofsufficient significance to be compensated for. In such an embodiment,the recorded time of the one or more in-line values may not match thatof the sample value, but the values will be considered to correspond.

In an exemplary embodiment, the extraction of the sample may beperformed on at least one condition being met during the event period.In an exemplary embodiment, the extraction of the sample may beperformed at a predetermined time in the event period. It should beappreciated that reference to a predetermined time in the event periodmay include detection or prediction of conditions associated with theevent period, rather than simply passage of a predetermined period oftime. For example, it is known in the art of milking analysis to inferor determine a current stage of an individual animal's milking based onsensed parameters such as yield and flow rate, particularly incomparison with historical data associated with the animal. Further,determination of a suitable time for extraction of a sample may be madefrom a wider population—for example a fixed delay from the start ofmilking may approximate a suitable point in milking. In an exemplaryembodiment, the sample value may be obtained between 30 to 120 secondsfrom the start of milking, once flow rate has reached a minimumthreshold (for example, 1.5 litres per minute), and milk fat has reacheda minimum threshold (for example, 3 g/100 mL).

In an exemplary embodiment, the sample may be extracted about themid-point of the expected event period. The inventors consider that inthe context of analysing milk, the mid-point of milking may haveancillary benefits in exemplary embodiments. For example, theinstantaneous in-line milk fat measurement error at the mid-point of themilking may be most representative of the in-line milk fat measurementerror of the whole cow milking. Further, where the sample sensor iscapable of sensing a milk parameter such as protein and/or lactosecontent in addition to fat, it is believed that the mid-point of milkingmay be preferred for analysis of these parameters. While theseparameters may not require determination of corrected representativevalues, it may still be of value to leverage the analytical capabilitiesof the sample sensor if they are available. However, it should beappreciated that this is not intended to be limiting to all exemplaryembodiments of the present disclosure, and it is contemplated that thesample may be extracted at other times within the event period.Generally, it is envisaged that the sampling time may be selected toavoid low flow periods at the beginning and end of a milking. Further,in exemplary embodiments a plurality of samples may be extracted withinthe event period.

In an exemplary embodiment, extracting the sample of the fluid from theflow of fluid during the event period may include performing one or morerinses of the on-line sensor prior to collection of the volume of fluidto be analysed as the sample. Reference to a rinse should be understoodto mean the processing of a volume of fluid through the on-line sensor,the rinse fluid being extracted from the fluid flow during the sameevent period as that being analysed as the sample. The inventorsenvisaged that this may assist with reducing the likelihood ofcontamination of the sample by the sample of the previous event period.

However, because the fat content changes during a cow milking, the milkin each rinse will have a different fat content. In exemplaryembodiments in which extraction of the sample involves two rinses and afinal sample, a fraction of the milk of the first rinse will be mixedwith the milk of the second rinse, and a fraction of the milk of thesecond rinse will be mixed with the milk of the final sample. The finalsample will have a fat content comprised of the two rinses and the finalsample. To account for this, in exemplary embodiments, the in-line valueof the fluid parameter corresponding to the time of extracting thesample may be a weighted average of the in-line values at the time ofthe rinses and final sample, with the later obtained in-line valuesgiven a higher weighting. It is envisaged that this may assist withaccounting for variation in the fluid parameter during collection of thesample.

By way of example in the context of milk analysis, where performing tworinses and a final sample, the in-line value of the fluid parametercorresponding to the time of extracting the sample may be:P=x·V₃+y·V₂+z·V₁, where P is the sample in-line value, V_(n) is thein-line value at the time of the rinses and final sample, and (x, y, z)are the relative weightings and x>y>z.

In an exemplary embodiment, ‘x’ may be between 0.8 and 0.9, ‘y’ may bebetween 0.09 and 0.16, and ‘z’ may be between 0.01 and 0.04. It shouldbe appreciated that these values are not intended to be limiting to allexemplary embodiments, as it is contemplated that these may beinfluenced by factors such as the type of fluid being measured, andcharacteristics of the sample sensor such as geometry and sensortransducer type and/or materials.

Determination of the representative in-line value of the fluid parameteracross the event period, based at least in part on the plurality ofin-line values, may be made using any suitable technique known in theart. For example, the representative in-line value may be an average ofthe plurality of in-line values. In particular, the representativein-line value may be a weighted average, and more particularly weightedby the flow rates corresponding to the in-line values. In exemplaryembodiments, the representative value may be determined from discretein-line values, or by interpolation between discrete in-line values. Inexemplary embodiments, the representative in-line value may bedetermined cumulatively throughout the milking, or at the end of themilking. In an exemplary embodiment in which the in-line value iscontinuously measured, this measurement may be integrated or averaged todetermine the representative value.

The inventors have identified that the difference between the in-linevalue of the fluid parameter corresponding to the time of extracting thesample, and the value of the fluid parameter as determined by a higherprecision analysis, is relatively consistent across the event period.With the sample value providing a higher precision measurement than thein-line value, this allows for an overall representative value of thefluid parameter across the event period to be determined using an errorcorrection value based on the assumption that the measurement error ofthe in-line sensor is relatively consistent across the event period.This overall representative value may be, for example a correction oferror in the in-line values across the event period (or therepresentative in-line value) to obtain the corrected representativevalue of the fluid parameter. However it should be appreciated that theoverall representative value may not result from correcting error in oneof the sensor values as such, while still having greater accuracy thanthe representative in-line value. For example, an estimated averagevalue of the fluid parameter over the event period may be obtainedthrough adjustment of the sample value of the fluid parameter by anerror correction value based on a relationship between the in-line valueof the fluid parameter corresponding to the time of extracting thesample and the representative in-line value of the fluid parameteracross the event period.

For completeness, it should be appreciated that reference to correctionof error is intended to mean an improvement in the accuracy of thedetermination of the fluid parameter in comparison with that determinedfrom the in-line sensor alone.

It should be appreciated that in exemplary embodiments this difference(i.e. that between the in-line value of the fluid parametercorresponding to the time of extracting the sample, and the value of thefluid parameter as determined by a higher precision analysis) may beexpressed as an absolute difference or error, or a relative differenceor error.

In an exemplary embodiment, determination of the correctedrepresentative value of the fluid parameter includes: determining adifference between the in-line value of the fluid parametercorresponding to the time of extracting the sample, and the sample valueof the fluid parameter; and adjusting the representative in-line valueof the fluid parameter across the event period by the determineddifference.

By way of example in the context of milk analysis, if the in-line valueof milk fat corresponding to the time of extracting the sample was 4.8g/100 mL, and the sample value of the fluid parameter was 4.0 g/100 mL,the difference would be −0.8 g/100 mL (the difference being indicativeof the measurement error of the in-line sensor). If the representativein-line value of the fluid parameter across the event period was 5.8g/100 mL, the corrected representative value of the fluid parameterwould be 5.8+(−0.8)=5.0 g/100 mL.

In an exemplary embodiment, determination of the estimated average valueof the fluid parameter includes: determining a difference between thein-line value of the fluid parameter corresponding to the time ofextracting the sample, and the representative in-line value of the fluidparameter across the event period; and adjusting the sample value of thefluid parameter by the determined difference.

By way of example in the context of milk analysis, if the in-line valueof milk fat corresponding to the time of extracting the sample was 4.8g/100 mL, and the representative in-line value of the fluid parameteracross the event period was 5.8 g/100 mL, the difference would be +1.0g/100 mL. If the sample value of the fluid parameter was 4.0 g/100 mL,the corrected representative value of the fluid parameter would be4.0+1.0=5.0 g/100 mL.

In an exemplary embodiment, determination of the correctedrepresentative value of the fluid parameter includes: determining arelative difference between the in-line value of the fluid parametercorresponding to the time of extracting the sample, and the sample valueof the fluid parameter; and adjusting the representative in-line valueof the fluid parameter across the event period with the relativedifference.

In an exemplary embodiment, determination of the estimated average valueof the fluid parameter includes: determining a relative differencebetween the in-line value of the fluid parameter corresponding to thetime of extracting the sample, and the representative in-line value ofthe fluid parameter across the event period; and adjusting the samplevalue of the fluid parameter with the relative difference.

For a firmware and/or software (also known as a computer program)implementation, the techniques of the present disclosure may beimplemented as instructions (for example, procedures, functions, and soon) that perform the functions described. It should be appreciated thatthe present disclosure is not described with reference to any particularprogramming languages, and that a variety of programming languages couldbe used to implement the present invention. The firmware and/or softwarecodes may be stored in a memory, or embodied in any other processorreadable medium, and executed by a processor or processors. The memorymay be implemented within the processor or external to the processor.

A processor may be a microprocessor, but in the alternative, theprocessor may be any processor, controller, microcontroller, statemachine, or cloud computing device known in the art. A processor mayalso be implemented as a combination of computing devices, for example,a combination of a digital signal processor (DSP) and a microprocessor,a plurality of microprocessors, one or more microprocessors inconjunction with a DSP core, or any other such configuration.

The processors may function in conjunction with servers and networkconnections as known in the art. By way of example, the on-line andsample sensors and a central processor may communicate with each otherover a Controller Area Network (CAN) bus system. In the context ofmilking, other performance sensors (for example flow or yield sensors),animal identification devices, and milking plant sensors may alsocommunicate with the central processor. In an exemplary embodiment,animal identifiers, data from the sensors, and any other data may bestored in a data cloud.

The steps of a method, process, or algorithm described in connectionwith the present disclosure may be embodied directly in hardware, in asoftware module executed by one or more processors, or in a combinationof the two. The various steps or acts in a method or process may beperformed in the order shown, or may be performed in another order.Additionally, one or more process or method steps may be omitted or oneor more process or method steps may be added to the methods andprocesses. An additional step, block, or action may be added in thebeginning, end, or intervening existing elements of the methods andprocesses.

BRIEF DESCRIPTION OF DRAWINGS

Further aspects of the present invention will become apparent from thefollowing description which is given by way of example only and withreference to the accompanying drawings in which:

FIG. 1A is a schematic diagram of an exemplary livestock managementsystem in which an aspect of the present disclosure may be implemented;

FIG. 1B is a schematic diagram of an exemplary sensor arrangementassociated for use in the exemplary livestock management system;

FIG. 2 is a schematic diagram of an exemplary on-line sensor accordingto one aspect of the present disclosure;

FIG. 3 is a state machine diagram for operation of the exemplary on-linesensor according to one aspect of the present disclosure;

FIG. 4 is a graph illustrating the relationship between values of fatcontent of milk determined by an in-line sensor and an on-line sensorover time;

FIG. 5 is a flow diagram illustrating a first exemplary method ofdetermining a representative in-line value of a fluid parameter acrossan event period;

FIG. 6 is a flow diagram illustrating a second exemplary method ofdetermining a representative in-line value of a fluid parameter acrossan event period;

FIG. 7 is a flow diagram illustrating an exemplary method of determininga representative in-line value of fat content of milk extracted from amilking animal across a milking period;

FIG. 8 is a graph illustrating the performance of the exemplary methodof determining a representative in-line value of fat content of milk,and

FIG. 9 is a plot of representative in-line sensor values againstlaboratory test values.

DETAILED DESCRIPTION

Exemplary embodiments are discussed herein in the context of analysis ofmilk. However, it should be appreciated that principles of thedisclosure discussed herein may be applied to the analysis of otherfluids.

FIG. 1A illustrates a livestock management system 100, within which alocal hardware platform 102 manages the collection and transmission ofdata relating to operation of a milking facility. The hardware platform102 has a processor 104, memory 106, and other components typicallypresent in such computing devices. In the exemplary embodimentillustrated the memory 106 stores information accessible by processor104, the information including instructions 108 that may be executed bythe processor 104 and data 110 that may be retrieved, manipulated orstored by the processor 104. The memory 106 may be of any suitable meansknown in the art, capable of storing information in a manner accessibleby the processor 104, including a computer-readable medium, or othermedium that stores data that may be read with the aid of an electronicdevice. The processor 104 may be any suitable device known to a personskilled in the art. Although the processor 104 and memory 106 areillustrated as being within a single unit, it should be appreciated thatthis is not intended to be limiting, and that the functionality of eachas herein described may be performed by multiple processors andmemories, that may or may not be remote from each other. Theinstructions 108 may include any set of instructions suitable forexecution by the processor 104. For example, the instructions 108 may bestored as computer code on the computer-readable medium. Theinstructions may be stored in any suitable computer language or format.Data 110 may be retrieved, stored or modified by processor 104 inaccordance with the instructions 110. The data 110 may also be formattedin any suitable computer readable format. Again, while the data isillustrated as being contained at a single location, it should beappreciated that this is not intended to be limiting—the data may bestored in multiple memories or locations. The data 110 may also includea record 112 of control routines for aspects of the system 100.

The hardware platform 102 may communicate with various devicesassociated with the milking facility, for example: in-line sensors 114 ato 114 n associated with individual milking clusters within the milkingfacility, and sample sensors in the form of on-line sensors 116 a to 116n associated with the individual milking clusters.

Animal identification devices 118 a to 118 n are provided fordetermining an animal identification (“animal ID”) of individual animalsentering, or within, the milking facility. More particularly, the animalidentification devices 118 a to 118 n may be used to associated ananimal ID with each of the milking clusters associated with the in-linesensors 114 a to 114 n and on-line sensors 116 a to 116 n, such that thesensor data may be attributed to the individual animals. A variety ofmethodologies are known for the determination of an animal ID—forexample a radio frequency identification (“RFID”) reader configured toread a RFID tag carried by the animal. In an alternative embodiment, orin conjunction with the animal identification devices 118 a to 118 n, auser may manually enter (or correct) animal IDs via a userdevice—examples of which are discussed below.

The hardware platform 102 may also communicate with user devices, suchas touchscreen 120 located within the milking facility for monitoringoperation of the system, and a local workstation 122. The hardwareplatform 102 may also communicate over a network 124 with one or moreserver devices 126 having associated memory 128 for the storage andprocessing of data collected by the local hardware platform 102. Itshould be appreciated that the server 126 and memory 128 may take anysuitable form known in the art—for example a “cloud-based” distributedserver architecture. The network 124 potentially comprises variousconfigurations and protocols including the Internet, intranets, virtualprivate networks, wide area networks, local networks, private networksusing communication protocols proprietary to one or morecompanies—whether wired or wireless, or a combination thereof. It shouldbe appreciated that the network 124 illustrated may include distinctnetworks and/or connections: for example a local network over which theuser interface may be accessed within the vicinity of the milkingfacility, and an internet connection via which the cloud server isaccessed. Information regarding operation of the system 100 may becommunicated to user devices such as a smart phone 130 or a tabletcomputer 132 over the network 124.

FIG. 1B illustrates the in-line sensors 114 a to 114 n and on-linesensors 116 a to 116 n connected over a Controller Area Network (CAN)bus with the hardware platform 102. It should be appreciated that whilenot illustrated, additional performance sensors (for example performancesensors such as milk flow or yield sensors) may also be connected to,and communicate over, the CAN bus. Each of the in-line sensors 114 a to114 n and on-line sensors 116 a to 116 n is associated with anindividual milking cluster in the milking facility—i.e. the sensor dataoutput by an individual sensor relates to milk from an individualanimal. In exemplary embodiments, sensors may be provided for eachmilking cluster in the milking facility. However, it should beappreciated that this is not intended to be limiting to every embodimentof the present disclosure. For example, it is contemplated that only asubset of milking clusters may have associated sensors (e.g. 1 in 4).

In an exemplary embodiment, the in-line sensor 114 may be configured todetermine at least the fat content of milk—for example the YieldSense™volume, fat, and protein sensor by LIC Automation Limited, or theAfiLab™ fat, protein and lactose concentration sensor by Afimilk Ltd. Itshould be appreciated that while the inventors have identified thepresent disclosure as having particular application to analysis of fat,this is not intended to be limiting to all embodiments of the presentdisclosure.

In an exemplary embodiment, the on-line sensor 116 may implement anultrasound based sensing methodology as performed by the off-lineLactiCheck™ milk analyser by Page & Pedersen International, Ltd or amid-infrared based sensing methodology as performed by the off-lineMIRIS™ Dairy Milk Analyzer by Miris Holding AB. Further, while thesample sensor is described in the context of being an on-line sensor, itshould be appreciated that the present disclosure may have applicationto embodiments in which the sample is analysed by an off-line sensor.

Referring to FIG. 2 , an exemplary on-line sensor 200 is illustrated,which may be used as the on-line sensors 116 a to 116 n. In thisexemplary embodiment, the on-line sensor 200 includes a sampleextraction device 202 configured to extract a sample of milk from a milktube 204 through which milk flows, and a sensing device 206 configuredto receive and analyse the sample. It should be appreciated that inexemplary embodiments the milk tube 204 may be a component of theon-line sensor 200—for example a section of tube configured to beconnected in-line with the long milk tube of a milking cluster.

An off-take 208 in the milk tube 204 is connected to a sample chamber210 via a first sample tube 212. A first peristaltic pump (hereinreferred to as sample pump 214) is provided on the first sample tube 212to draw milk from the off-take 208 to the sample chamber 210, with afirst non-return valve 216 preventing milk from being drawn back fromthe sample chamber 210. The sample chamber 210 may include electrodesmeasuring conductivity to permit detection of a fill level of the samplechamber 210.

A sample waste tube 218 connects the sample chamber 210 to waste 220,with an associated second peristaltic pump (herein referred to as wastepump 222) provided to draw milk from the sample chamber 210 to waste220. An air bleed tube 224 having a second non-return valve 226 connectsto the top of the sample chamber to permit escape of air during fillingof the sample chamber 210. A third non-return valve (not illustrated inFIG. 2 ) is located in the wall of the sample chamber 210 to permit airto be drawn into the chamber when the waste pump 222 (or sensor pump232, see below) are running.

In this exemplary embodiment, the sensing device 206 includes a sensorcell 228 configured to perform ultrasound based measurements of milkcontained therein. For example, the sensor cell 228 may be theultrasound sensing cell of the LactiCheck™ milk analyser. A sampledelivery tube 230 is connected near or at the bottom of the samplechamber 210 and connects the sample chamber to the sensor cell 228. Athird peristaltic pump (herein referred to as sensor pump 232) isprovided to deliver milk to the sensor cell 228 from the sample chamber210. A sensor waste tube 234 connects the sensor cell 228 to waste 220.

While not illustrated in FIG. 2 , it should be appreciated that one ormore controllers may be used to control the operation of the variouscomponents described, receive data obtained by the sensor cell 228, andcommunicate over a network such as the CAN bus of FIG. 113 .

FIG. 3 illustrates a state machine diagram 300 for control of theon-line sensor 200. In idle state 302, the on-line sensor 200 awaits asignal that milking has started. In first state 304, the on-line sensor200 receives a signal that indicates that milking has started (forexample, from the in-line sensor 114), and initiates a delay timerbefore transitioning to the second state 306 on expiry of the timer. Inan alternate embodiment, the signal may indicate a predetermined pointin milking—for example the mid-point—and the state machine may proceedto the second state 306 without use of a delay timer.

In second state 306, the waste pump 222 is operated for a predeterminedperiod of time to remove residual milk from the sample chamber 210. Inthird state 308 the waste pump 222 and sensor pump 232 are operated fora predetermined period of time to remove residual milk from the sensorcell 228 and associated sample delivery tubes.

In fourth state 310 the sample pump 214 and waste pump 222 are operatedto draw milk from the milk tube 204 via the offtake 208. The milk ispumped through the sample chamber 210 to the waste tube 218 to removeany milk from the previous milking which remains in the first sampletube 212. The new milk also provides a rinsing effect of the waste tube218 between the sample chamber 210 and waste 220.

In fifth state 312, to collect a sample the sample pump 214 is run (withthe waste pump 222 and sensor pump 232 stopped) until the milk fills thesample chamber 210 to the fill level (as detected by electrodes). Insixth state 314 a time delay (for example about 1 second) allows air inthe milk to escape via rising to the top of the sample, as the accuracyof ultrasound measurements can be affected by bubbles in the sample.

In seventh state 316 the sample is then withdrawn from the samplechamber 210 by operating the sensor pump 234 for a predetermined periodof time to rinse the sample delivery tube 230 and sensor cell 228 anddeliver a slug of milk to fill the sensor cell 228.

In an exemplary embodiment, states 310 to 316 may be repeated one ormore times—for example three times—in order to rinse the on-line sensor200 to reduce the effect of cross-contamination from residual milk fromthe previous milking.

In eight state 318, analysis of the milk in sensor cell 228 isinitiated. In ninth state 320, the on-line sensor 200 waits for theresults of the analysis.

In tenth state 322, the results of the analysis are obtained, at whichtime the waste pump 222 and sensor pump 232 are operated to deliver thecurrent sample to waste 220.

FIG. 4 is a graph of idealised fat content measurements of milk versustime for the purposes of understanding a principle of operation of anaspect of the present disclosure. First plot 400 is representative of anoutput over time of an in-line YieldSense™ volume, fat, and proteinsensor by LIC Automation Limited. Second plot 402 is an approximation ofmeasurements of spot samples extracted from the same flow of milk beinganalysed by the YieldSense™ sensor, and analysed using a higherprecision sensor such as the off-line LactiCheck™ milk analyser by Page& Pedersen International, Ltd.

First line 404 illustrates the fat content across the entire milking,based on the YieldSense™ measurements—for example a weighted average ofthe first plot 400 (weighted by flow rates at times corresponding to theinstant measurements), herein referred to as the “in-line average fat”.For completeness, it should be appreciated that the representative fatcontent 404 may be obtained by means other than a weighted average.Second line 406 illustrates fat content as determined from a higherprecision measurement, herein referred to as the “actual fat”. By way ofexample, the higher precision measurement may be obtained by way oflaboratory testing of a sample of milk collected from a vessel in whichmilk from the entire milking is collected, and mixed prior to collectionof the sample.

The inventors have identified that the error 408 between a YieldSense™measurement 410 and a LactiCheck™ measurement 412 at a particular pointof time is representative of the error 414 between the in-line averagefat 404 and the actual fat 406.

As a result, in principle a corrected value of the in-line average fat404 may be obtained to better approximate the actual fat 406—for exampleby adjusting the in-line average fat 404 by the error 414, or bydetermining a relative error and correcting the in-line average fat 404accordingly.

Alternatively, the difference 416 between a point 418 on the first plot400 and the in-line average fat 404 is representative of the difference420 between a corresponding point 422 of the second plot 402 and theactual fat 406. As such, an approximation or estimation of the actualfat 406 may be obtained by adjusting the point 422 value by thedifference 420. Again, it should be appreciated that a determination ofrelative error may be used to make this correction.

FIG. 5 illustrates a method 500 of determining a correctedrepresentative value of a fluid parameter—for example fat content ofmilk. In step 502, an event period is started, for example a milkingsession of an individual animal. The start of the event period may bedetected, for example, by the in-line sensor 114 (or another in-linesensor) detecting the start of a flow of milk, and a signal sent to theassociated on-line sensor 116.

In step 504, the in-line sensor 114 obtains a plurality of measurementsof the milk (the “in-line values”) flowing past the in-line sensor 114.Such measurements will herein be referred to as occurring continuously,but it should be apparent to a person skilled in the art of datacollection and analysis that this is not intended to exclude embodimentsin which discrete measurements are made repeatedly (whether periodicallyor intermittently) at a sufficient rate to represent a continuousmeasurement. For example, a YieldSense™ measurement being used as an“in-line value” may be a value representative of a plurality ofinstantaneous measurements over a relatively short period of time (forexample, the preceding 5 seconds) in comparison with the time to extractand analyse a sample with an on-line sensor 116 (for example, in theorder of 120 seconds. Such in-line values may be transmitted to aprocessing resource shared with the on-line sensor 116, such as thehardware platform 102, including a time at which the in-line value wasobtained.

The in-line values are measured until the end of the event period (forexample, end of milking) in step 506. In step 508, a representativein-line value of the fluid parameter across the event period isdetermined (for example, in-line average fat 404 of FIG. 4 ) andtransmitted to the shared processing resource.

In step 510, following detection of the start of the event period instep 502, the on-line sensor 116 obtains a sample of the milk. In anexemplary embodiment, the sample is extracted after a predeterminedperiod of time—for example a period of time after which a mid-point ofthe milking is expected. In step 512 the sample is analysed by theon-line sensor 116, and in step 514 a “sample value” of the targetedparameter (for example, fat content) is determined and transmitted tothe shared processing resource, including a time at which the sample wasextracted. In exemplary embodiments, steps 510 to 514 may be repeated toobtain more than one sample value during the event period.

In step 516, for each sample value the shared processing resourcedetermines one or more corresponding in-line values, based on timing ofthe in-line values and the extraction of the sample. In exemplaryembodiments—described further below with reference to FIG. 7 and FIG. 8—the corresponding in-line value may be determined based on a pluralityof in-line values obtained during collection of the sample, hereinreferred to as a “sample in-line value”.

In step 518, a comparison of the sample value and corresponding in-linevalue (or sample in-line value) is made to determine the differencebetween them—the difference being indicative of the error of the in-linevalues across the milking (whether absolute or relative).

In step 520, a corrected representative value of the fluid parameter(for example, a corrected value of the in-line average fat 404) isdetermined based on the determined error. For example, therepresentative in-line value determined in step 508 may be adjusted bythe absolute error.

In step 522 the corrected representative value for that event period isstored—for example in memory 106 of the hardware platform 102, and/ormemory 128 associated with the server 126. It should be appreciated thatthe corrected representative value may be stored against a record for anindividual animal determined as being the source from which the analysedmilk was extracted. Further, alerts or further actions may be determinedon the basis of the received value—as known in the art.

FIG. 6 illustrates another method 600 of determining a correctedrepresentative value of a fluid parameter—for example fat content ofmilk. Steps 602 to 616 are substantially equivalent to steps 502 to 516of method 500 as described above.

However, in step 618 the in-line value (or the sample in-line value)corresponding to the sample value is compared with the representativein-line value (for example, in-line average fat 404 of FIG. 4 ) todetermine the difference between them—being indicative of the differencebetween the sample value and the corrected representative value of thefluid parameter. This difference may be determined, for example, inabsolute or relative terms.

In step 620, the sample value is adjusted based on the determineddifference between the in-line value and the representative in-linevalue to produce an estimated representative value. For example, thesample value may be adjusted by the absolute difference. In step 622 theestimated representative value for that event period is be stored andactioned as required.

FIG. 7 illustrates an exemplary method 700 of determining a correctedrepresentative value of a fluid parameter—for example fat content ofmilk—in which a rinsing procedure is performed for the on-line sensor116 to reduce the effects of cross-contamination (for example,repetition of states 308 to 316 of FIG. 3 ). In such an embodiment, thecorresponding in-line value may be a weighted combination of in-linevalues. For example, in an embodiment in which two rinses are performedbefore collecting the sample for analysis, the in-line value (“samplein-line value”) determined to correspond to the extraction of the samplemay be determined as follows: P=x·V₃+y·V₂+z·V₁, where P is the samplein-line value, V_(n) is the in-line value at the time of extracting therinses and final sample, and (x, y, z) are the relative weightings andx>y>z.

By way of example, the values of (x, y, z) may be (0.86, 0.12, 0.02).However, it should be appreciated that the values of (x, y, z) may beinfluenced by the configuration of the on-line sensor, such as thevolume of the lines and chambers/cells exposed to milk during sampleextraction and analysis, and these values may be derived for aparticular sensor configuration. Method 700 is described with particularreference to the in-line sensor being a YieldSense™ sensor 114, theon-line sensor being on-line sensor 200 with the sensing device 206performing ultrasound analysis using a LactiCheck™ sensor, and the fluidparameter being milk fat content.

In step 702, the YieldSense™ sensor 114 detects the start of milking,and sends a milking start signal over the network (for example, the CANbus of FIG. 1B) to an associated on-line sensor 200 and hardwareplatform 102. In step 704, the YieldSense™ sensor 114 periodicallydetermines a current in-line fat value and transmits this to thehardware platform 102—for example every 5 seconds. In step 706 theYieldSense™ sensor 114 determines a representative in-line fat valuebased on the current in-line fat values obtained during the milking—forexample a cumulative average weighted by flow rate. In step 708 theYieldSense™ sensor 114 detects the end of milking, and in step 710transmits this over the network together with the final representativein-line fat value.

In step 712, on receiving the signal from the YieldSense™ sensor 114indicating the start of milking, the on-line sensor 200 waits for apredetermined period of time. In step 714, the sample extraction device202 extracts a first rinse sample from the milk tube at a first sampletime (t1), and rinses the sensing device 206 in step 716. In step 718,the sample extraction device 202 extracts a second rinse sample from themilk tube at a second sample time (t2), and rinses the sensing device206 in step 720. In step 722, the sample extraction device 202 extractsa third sensing sample from the milk tube at a third sample time (t3),and fills the sensor cell 228 in step 724. In step 726, the analysis ofthe third sensing sample is performed, and a sample value of fat contentdetermined. In step 728 the sample value is transmitted over thenetwork.

In step 730, the current in-line fat values from the YieldSense™ sensor114 are recorded as they are received—for example by the hardwareplatform 102. In step 732, at each of steps 714, 718, and 722, thecurrent in-line fat values at the times of extracting the respectivesamples (t1, t2, t3) are recorded. The times (t1, t2, t3) may bedetermined, for example, by using the associated state transitiondescribed in FIG. 3 .

In step 734, a sample in-line value (P) of fat content for the thirdsensing sample of milk is determined using: P=0.86·V₃+0.12·V₂+0.02·V₁.In step 736, a correction value is determined based on a comparison ofthe sample value and the sample in-line value, or the sample in-linevalue and the final representative in-line value provided by theYieldSense™ sensor 114 in step 710.

In step 738 an overall representative fat content value—i.e. arepresentation of the average fat content of milk extracted across thecourse of the milking—is determined based on the correction valuedetermined in step 736.

In one embodiment, where the correction value is the difference betweenthe sample value and the sample in-line value, a correctedrepresentative fat content value may be determined by adjusting thefinal representative in-line fat value from the YieldSense™ sensor 114by the correction value (for example, as described in relation to method500).

In another embodiment, where the correction value is the differencebetween the sample in-line value and the final representative in-linevalue, an estimated representative fat content value may be determinedby adjusting the sample value from the on-line sensor 200 by thecorrection value (for example, as described in relation to method 600).

In step 740 the overall representative fat content value is stored, andreported and/or actioned as required.

FIG. 8 is a graph illustrating implementation of the method 700. Firstplot 800 shows the in-line values of fat content determined by aYieldSense™ sensor based on a weighted average of instantaneousmeasurements over a 5 second period—weighted by the flow rate of themilk (illustrated by second plot 802). A representative in-line fatcontent value based on the in-line values is shown by line 804. It isnoted that the trend in fat content illustrated in plot 800 is not thatexpected for typical milking—more particularly there is a drop in fat inthe course of the milking, while this would normally be expected to riseover the same period.

Dashed line 806 represents the current state of the on-line sensor 200over time—including the first rinse sample 808, second rinse sample 810,and the sensing sample 812. The sample fat value 814 is compared withthe sample in-line fat value 816 to determine a measurement error forthis milking session. A corrected representative in-line fat contentvalue is derived by adding the measurement error to the representativein-line fat content value 804, which approximates a laboratorydetermined fat content value 818 (for example, based on a sample from avessel in which milk from across the milking is mixed).

FIG. 9 is a plot demonstrating outcomes of implementing the presentdisclosure. The plot shows the results for 92 milkings of 20 cows overfive days. A first series 900 having a first linear relationship 902compares the fat content (in g/100 mL) obtained by an in-line YieldSensesensor with a higher precision laboratory method performed on a bucketsample taken at the end of each milking. A second series 904 having asecond linear relationship 906 compares the corrected fat content withthe laboratory method. In this example, the corrected fat content wasobtained by correcting the YieldSense value using method 600, moreparticularly using absolute error in step 620, with the final samplebeing taken at a time approximately 25 to 40 percent through the milkingtime using a fixed delay time in step 304 of the state machine in FIG. 3.

It may be seen in comparison with the 1:1 line 908 that the plot shows asignificant improvement in systematic error. However, the inventorsconsider that the improvement of standard deviation of error (from 0.608g/100 mL to 0.283 g/100 mL) may be particularly advantageous,compensating for errors in the YieldSense measurement which may varyfrom milking to milking. The entire disclosures of all applications,patents and publications cited above and below, if any, are hereinincorporated by reference.

Reference to any prior art in this specification is not, and should notbe taken as, an acknowledgement or any form of suggestion that thatprior art forms part of the common general knowledge in the field ofendeavour in any country in the world.

The invention may also be said broadly to consist in the parts, elementsand features referred to or indicated in the specification of theapplication, individually or collectively, in any or all combinations oftwo or more of said parts, elements or features.

Where in the foregoing description reference has been made to integersor components having known equivalents thereof, those integers areherein incorporated as if individually set forth.

It should be noted that various changes and modifications to thepresently preferred embodiments described herein will be apparent tothose skilled in the art. Such changes and modifications may be madewithout departing from the spirit and scope of the invention and withoutdiminishing its attendant advantages. It is therefore intended that suchchanges and modifications be included within the present invention.

Aspects of the present invention have been described by way of exampleonly and it should be appreciated that modifications and additions maybe made thereto without departing from the scope thereof.

1. A system for analysing a fluid, including: an in-line sensorconfigured to analyse a fluid flowing past the in-line sensor todetermine at least one in-line value of a fluid parameter of the fluidacross an event period; a sample sensor configured to: analyse a sampleof fluid extracted from the flow of fluid during the event period, todetermine a sample value of the fluid parameter for the sample; and atleast one processor configured to: determine a representative in-linevalue of the fluid parameter across the event period based at least inpart on the at least one in-line value; determine an overallrepresentative value of the fluid parameter across the event periodbased on the representative in-line value, the sample value for thesample, and one or more of the in-line values corresponding to the timeof extracting the sample, wherein determination of the overallrepresentative value is based on an error correction value determinedfor the in-line sensor during the event period.
 2. The system of claim1, wherein the sample sensor includes a sample extraction deviceconfigured to extract the sample from the flow of fluid, and a sensingdevice configured to receive and analyse the sample.
 3. The system ofclaim 2, wherein the sample extraction device includes a samplecollection chamber for conditioning the sample of fluid prior todelivery to the sensing device.
 4. The system of claim 2, configuredsuch that extraction of the sample of the fluid from the flow of fluidduring the event period includes performing one or more rinses of thesensing device prior to collection of the volume of fluid to be analysedas the sample.
 5. The system of claim 4, wherein the one or more of thein-line values corresponding to the time of extracting the sample usedin determining an overall representative value of the fluid parameter isa weighted average of the in-line values at the time of the rinses andthe sample, wherein later obtained in-line values are given a higherweighting.
 6. The system of claim 1, wherein the extraction of thesample is performed on at least one condition being met during the eventperiod.
 7. The system of claim 6, wherein the extraction of the sampleis performed by about the mid-point of an expected event period.
 8. Thesystem of claim 1, wherein the determination of the overallrepresentative value of the fluid parameter includes: determining adifference between the in-line value of the fluid parametercorresponding to the time of extracting the sample, and the sample valueof the fluid parameter; and adjusting the representative in-line valueof the fluid parameter across the event period by the determineddifference.
 9. The system of claim 1, wherein the determination of theoverall representative value of the fluid parameter includes:determining a difference between the in-line value of the fluidparameter corresponding to the time of extracting the sample, and therepresentative in-line value of the fluid parameter across the eventperiod; and adjusting the sample value of the fluid parameter by thedetermined difference.
 10. The system of claim 1, wherein the fluidbeing analysed is milk and the fluid parameter to be analysed is fatcontent.
 11. The system of claim 1, wherein the sample sensor isconfigured to analyse the sample of fluid using an ultrasoundmeasurement technique.
 12. A method for analysing a fluid, including thesteps of: analysing a fluid flowing past an in-line sensor to determineat least one in-line value of a fluid parameter of the fluid across anevent period; analysing, with a sample sensor, a sample of fluidextracted from the flow of fluid during the event period, to determine asample value of the fluid parameter for the sample; determining arepresentative in-line value of the fluid parameter across the eventperiod, based at least in part on the at least one in-line value; anddetermining an overall representative value of the fluid parameter basedon the representative in-line value, the sample value for the sample,and one or more of the in-line values corresponding to the time ofextracting the sample, wherein determination of the overallrepresentative value is based on an error correction value determinedfor the in-line sensor during the event period.
 13. The method of claim12, wherein the sample of fluid is extracted from the flow of fluidusing a sample extraction device of the sample sensor, and delivered toa sensing device of the sample sensor for analysis.
 14. The method ofclaim 13, including conditioning the sample of fluid in a samplecollection chamber prior to delivery to the sensing device.
 15. Themethod of claim 13, wherein extraction of the sample of the fluid fromthe flow of fluid during the event period includes performing one ormore rinses of the sensing device prior to collection of the volume offluid to be analysed as the sample.
 16. The method of claim 15, whereinthe one or more of the in-line values corresponding to the time ofextracting the sample used in determining an overall representativevalue of the fluid parameter is a weighted average of the in-line valuesat the time of the rinses and the sample, wherein later obtained in-linevalues are given a higher weighting.
 17. The method of claim 12, whereinthe extraction of the sample is performed by about the mid-point of anexpected event period.
 18. The method of claim 12, wherein thedetermination of the overall representative value of the fluid parameterincludes: determining a difference between the in-line value of thefluid parameter corresponding to the time of extracting the sample, andthe sample value of the fluid parameter; and adjusting therepresentative in-line value of the fluid parameter across the eventperiod by the determined difference.
 19. The method of claim 12, whereinthe determination of the overall representative value of the fluidparameter includes: determining a difference between the in-line valueof the fluid parameter corresponding to the time of extracting thesample, and the representative in-line value of the fluid parameteracross the event period; and adjusting the sample value of the fluidparameter by the determined difference.
 20. The method of claim 12,wherein the fluid being analysed is milk and the fluid parameter to beanalysed is fat content.